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White-collar workers are starting to feel nervous—and with reason. While 98 percent of college graduates who want a job still have one, and wages are creeping up, the labor market is showing signs of stress. Americans with bachelor’s degrees now make up a quarter of the unemployed—a record high. Meanwhile, high-school graduates are finding jobs faster than college graduates, an unprecedented shift.
Occupations most vulnerable to AI automation are seeing steep spikes in unemployment. Some layoffs are being justified with the “efficiency-enhancing” powers of AI tools like ChatGPT and Claude—but in many cases, this is just corporate “AI washing,” a way to distract from poor management decisions. Still, the trend is real. In recent weeks, Baker McKenzie cut 700 jobs, Salesforce laid off hundreds of employees, and KPMG negotiated lower fees with its own auditor. Even non-technical reporters recently recreated Monday.com’s workflow platform in under an hour, sending the company’s stock tumbling.
Perhaps these changes will happen slowly, giving workers time to adapt. Perhaps AI-driven layoffs will be limited to a sliver of the economy. Or perhaps the disruption will hit faster—and harder—than anyone expects. The truth is, no one knows exactly what will happen. AI is evolving at an exponential pace, reshaping the workforce in ways that are difficult to parse. But if white-collar jobs disappear quickly, the consequences could be more severe and long-lasting than a typical economic downturn.
The U.S. is adept at responding to cyclical recessions. In past downturns, Congress has cut taxes, issued stimulus checks, and expanded unemployment benefits. The Federal Reserve has slashed interest rates and bought large quantities of safe assets to spur borrowing and investment. These measures boost demand and help bring unemployment down.
But AI-driven white-collar layoffs could create a different problem: structural unemployment. Unlike cyclical unemployment, where the economy temporarily lacks demand, structural unemployment occurs when the skills workers possess are no longer needed. Companies might not want to hire accountants, engineers, lawyers, managers, human-resources specialists, financial analysts, PR executives, or customer-service agents—the very people they just let go. Writers might survive the disruption, but most other office workers could face years of unemployment.
Historically, structural unemployment has hit blue-collar workers hardest. White-collar jobs have generally been stable, even during recessions. During the Great Recession, unemployment for college graduates never rose above 5.3 percent, while high-school graduates saw rates as high as 11.9 percent. AI could invert this dynamic, leaving the educated and well-to-do worse off than less-educated neighbors.
The existing social safety net is ill-equipped for such a scenario. Unemployment benefits last only a few months and rarely match six-figure salaries. Young workers entering the labor market would find fewer entry-level white-collar jobs, depressing earnings for years or even decades. If higher-income households cut spending, the effects would ripple through the broader economy—harming retailers, restaurants, and service providers, while potentially triggering declines in the housing market and tax revenue. Inequality could soar as executives using AI to cut costs see wealth accumulation accelerate, leaving rank-and-file workers behind.
The lessons of the past are sobering. In the 1970s, automation devastated blue-collar communities in Detroit, Pittsburgh, and Gary, Indiana. Later, globalization accelerated job losses, leaving lasting damage. Workers became poorer, less healthy, and less happy; their children also suffered.
To address AI-driven unemployment, the country would need solutions from scratch. Current retraining programs have shown limited effectiveness. Community colleges help, but most office workers already hold two- or four-year degrees. Upskilling alone may not be enough.
Some in Silicon Valley advocate for a universal basic income—providing $1,500 per month to every adult—to offset AI-driven disruption. It would ensure a baseline of financial security and redistribute gains from rising productivity. OpenAI CEO Sam Altman has argued that such a system could free people to focus on art, care work, and social good.
But UBI is a stopgap, not a solution. To truly support families in a post-work economy, the government would need far larger payouts, financed by corporate taxes that would meet fierce resistance. More importantly, Americans value work beyond income—they want purpose, social connection, and the chance to contribute. Long-term unemployment can harm mental and physical health and create social unrest.
Perhaps society could adapt over time. People might find fulfillment in leisure, volunteering, or creative pursuits. But without work, social capital may collapse alongside economic capital, and inequality could deepen, creating a techno-oligarchy and a dispossessed underclass.
Throughout history, technological progress has made people more productive and prosperous without reducing overall labor demand. AI may be different—but whether it leads to widespread unemployment or simply transforms work, one thing is clear: the stakes for the white-collar workforce have never been higher.